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sdv-mcp-demo

by emqx 7 stars emqx/sdv-mcp-demo

MCP over MQTT + AI demo application for analyzing software defined vehicle data

READMEmain

Architecture

Application Flow Description

Data Collection

  • Obtain vehicle driving behavior data (Sample data)
    • Development environment: Using simulated data
    • Production environment: Generated in real-time by sdv-flow and stored locally on the vehicle

Data Processing

  1. Location Information Parsing

    • Extract latitude and longitude coordinates from driving behavior data
    • Parse corresponding administrative division information through Gaode MCP service
  2. Weather Information Correlation

    • Based on administrative division information
    • Obtain real-time weather data at the time of the event through MCP Server which encapsulates third-party API services

Report Generation

  • Integrate processed data (driving behavior, location, weather)
  • Use large language models to analyze and generate UBI (Usage-Based Insurance) driving behavior reports

Running

Create Local .env File

# You could get the JuHe API key at https://www.juhe.cn/docs/api/id/277
JUHE_API_KEY=

# You could get the GaoDe API key at https://lbs.amap.com/api/mcp-server/create-project-and-key
GAODE_KEY=

# Silicon Flow API
SFAPI_KEY=

# Model Name
MODEL_NAME=Pro/deepseek-ai/DeepSeek-V3

# MQTT broker address
MQTT_BROKER=127.0.0.1

Initialize Environment

uv sync

source .venv/bin/activate

Execution

  • Start emqx
docker run -d --name emqx -p 1883:1883 -p 8083:8083 -p 8084:8084 -p 8883:8883 -p 18083:18083 emqx/emqx:latest
  • Run Weather MCP Server
uv run weather.py
  • Run Vehicle Driving Behavior Analysis MCP Server
uv run vehicle.py
  • Run Report Generation Script
uv run app.py

After executing app.py, a sample report will be generated.

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